先说结论。它和通用视觉自监督的关系在于：从正样本采样 support 条件解释 contrastive SSL 何时能恢复 latent geometry，补足经验方法的理论边界。 高相关；详见方法、贡献和实验边界。
Figureure 1 · Overview of contrastive learning and the role of sampling diversity anFigure 1. Overview of contrastive learning and the role of sampling diversity and inductive bias. The generative process $g$ maps latent variables to observations, and the encoder $f$ learns to recover the latent structure. Here $f _ { 1 }$ denotes a low inductive bias encoder (e.g., MLP) and $f _ { 2 }$ a high inductive bias encoder (e.g., a model of the inverse process). Orange dot indicates the anchor point; green dots are co-occurring (positive) samples. Border colors on images match their latent positions. (a) Diversity holds: $f _ { 1 }$ recovers geometry. (b) Diversity violated (blue band): $f _ { 1 }$ fails. (c) Diversity violated: $f _ { 2 }$ recovers the latent structure despite restricted sampling diversity.这张图概括 The Loss Is Not Enough 的整体方法流程。阅读时先看模块之间传递的训练信号，再看作者如何把目标拆成可优化的子问题。Figureure 3 · Linear probe accuracy on CIFAR-10 by architecture and augmentation regFigure 3. Linear probe accuracy on CIFAR-10 by architecture and augmentation regime. Individual runs shown as points; bars indicate mean ±1 std. The “All” regime best approximates the diversity condition and yields highest accuracy across all architectures.这张图概括 The Loss Is Not Enough 的整体方法流程。阅读时先看模块之间传递的训练信号，再看作者如何把目标拆成可优化的子问题。
核心问题
它和通用视觉自监督的关系在于：从正样本采样 support 条件解释 contrastive SSL 何时能恢复 latent geometry，补足经验方法的理论边界。
方法拆解
从正样本采样 support 条件解释 contrastive SSL 何时能恢复 latent geometry，补足经验方法的理论边界
